Invariant Descriptors and Classifiers Combination for Recognition of Isolated Printed Tifinagh Characters

Oujaoura, Mustapha; Ayachi, Rachid El; Minaoui, Brahim; Fakir, Mohamed; Bouikhalene, Belaid; Bencharef, Omar

doi:10.14569/specialissue.2013.030205

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…The dataset elements can be easily converted to MathML or Latex. In this set we have created 6925 mathematical expressions using symbols from five languages Latin, Arabic, Tifinagh [20,21], Hebrew and Japanese (Fig. 4 a, b, c, d, e).…”

Section: Retrieving Resultsmentioning

confidence: 99%

A Semantic Approach for Mathematical Expression Retrieval

Asebriy¹,

Kaloun²,

Raghay³

et al. 2016

ijacsa

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Abstract-Math search or mathematical expression retrieval has become a challenging task. Mathematical expressions are very complex, they are highly symbolic, and they have a semantic meaning that we should respect. In this paper, we propose a similarity search method for mathematical expression based on a multilevel representation of expressions and a multilevel search. We used the K-Nearest Neighbors with three types of distances to evaluate relevance between expressions. In the experimental level, the proposed system significantly outperforms statistical algorithms.

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Section: Retrieving Resultsmentioning

confidence: 99%

A Semantic Approach for Mathematical Expression Retrieval

Asebriy¹,

Kaloun²,

Raghay³

et al. 2016

ijacsa

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“…In this paper, the used approaches are: Hu moments [21], Legendre moments [22,23,24], Zernike moments [25,26], walsh transform, Texture and GIST descriptors.…”

Section: Extractionmentioning

confidence: 99%

“…For a discrete image of M x N pixels with intensity function f(x, y), Hu [21] defined the following seven moments that are invariant to the change of scale, translation and rotation: …”

Section: Hu Momentsmentioning

confidence: 99%

Recognition of Isolated Printed Tifinagh Characters

Oujaoura¹,

Minaoui²,

Fakir³

et al. 2014

IJCA

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Most of the reported works in the field of character recognition systems achieve modest results by using a single method for calculating the parameters of the character image and a single approach in the classification phase of the system. So, in order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed Tifinagh characters by using a fusion of some classifiers and a combination of some features extraction methods. The Legendre moments, Zernike moments, Hu moments, Walsh transform, GIST and texture are used as descriptors in the features extraction phase due to their invariance to translation, rotation and scaling changes. In the classification phase, the neural network, the Bayesian network, the multiclass SVM (Support Vector Machine) and the nearest neighbour classifiers are combined together. The experimental results of each single features extraction method with each single classification method are compared with our approach to show its robustness. A recognition rate of 100 % is achieved by using some combined descriptors and classifiers.

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“…So a large image database can be organized according to the classication rule and therefore, the searching can be performed [31]. The moments are widely used in many applications for feature's extraction due to their invariance to scale, rotation and reection change [19,22,20,21]. The use of moments for image analysis and pattern recognition was inspired by Hu [11].…”

Section: Extraction and Computation Of Momentsmentioning

confidence: 99%

Image Annotation by Moments

Oujaoura¹,

Minaoui²,

Fakir³

2014

Moments and Moment Invariants - Theory and Applications

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The rapid growth of the Internet and multimedia information has generated a need for technical indexing and searching of multimedia information, especially in image retrieval. Image searching systems have been developed to allow searching in image databases. However, these systems are still inecient in terms of semantic image searching by textual query. To perform semantic searching, it is necessary to be able to transform the visual content of the images (colours, textures, shapes) into semantic information. This transformation, called image annotation, assigns a legend or keywords to a digital image. The traditional methods of image retrieval rely heavily on manual image annotation which is very subjective, very expensive and impossible given the size and the phenomenal growth of currently existing image databases. Therefore it is quite natural that the research has emerged in order to nd a computing solution to the problem. It is thus that research work has quickly bloomed on the automatic image annotation, aimed at reducing both the cost of annotation and the semantic

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Invariant Descriptors and Classifiers Combination for Recognition of Isolated Printed Tifinagh Characters

Cited by 10 publications

References 13 publications

A Semantic Approach for Mathematical Expression Retrieval

A Semantic Approach for Mathematical Expression Retrieval

Recognition of Isolated Printed Tifinagh Characters

Image Annotation by Moments

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